This project examines the power and performance of Likelihood Ratio (LR) and Wald tests under a logistic regression-based coincidence paradigm for binary outcomes. The dataset consists of 90 clinical patients, analyzing how age, sex, and treatment duration influence relapse outcomes.
Understanding the relationship between age, sex, and treatment outcomes is valuable for clinicians optimizing treatment strategies, researchers studying relapse risk factors, and policymakers evaluating healthcare interventions.
Two interactive exploratory data analysis graphs are presented: A side-by-side boxplot illustrating the relationship between age and relapse status, further distinguished by sex (Male/Female) using color coding. Also, a scatter plot showing the relationship between age and treatment duration (years), with additional context provided by color coding each data point based on relapse status (Yes/No). Additionally, a sortable and searchable data table using DT() is displayed below, allowing for interactive exploration of individual patient details.
# Load dataset
data <- read.csv("/Users/annachen/Desktop/semester 4/DATA 555/sex_age_data.csv")
# Summary statistics
summary(data)
## i sex age duration
## Min. : 1.00 Min. :0.0000 Min. :24.10 Min. :3.200
## 1st Qu.:23.25 1st Qu.:0.0000 1st Qu.:43.90 1st Qu.:4.600
## Median :45.50 Median :1.0000 Median :50.50 Median :5.000
## Mean :45.50 Mean :0.5111 Mean :50.95 Mean :5.052
## 3rd Qu.:67.75 3rd Qu.:1.0000 3rd Qu.:58.80 3rd Qu.:5.600
## Max. :90.00 Max. :1.0000 Max. :76.40 Max. :7.100
## age_sex relapse agegtmed
## Min. : 0.00 Min. :0.0 Min. :0.0
## 1st Qu.: 0.00 1st Qu.:0.0 1st Qu.:0.0
## Median :30.95 Median :0.0 Median :0.5
## Mean :26.48 Mean :0.4 Mean :0.5
## 3rd Qu.:52.48 3rd Qu.:1.0 3rd Qu.:1.0
## Max. :76.40 Max. :1.0 Max. :1.0